905 research outputs found
First-principles study of phonon linewidths in noble metals
Phonon lifetimes in Cu, Ag, and Au at low and high temperatures were calculated along high symmetry directions using density functional theory combined with second-order perturbation theory. Both harmonic and third-order anharmonic force constants were computed using a supercell small displacement method, and the two-phonon densities of states were calculated for all three-phonon processes consistent with the kinematics of energy and momentum conservation. A nonrigorous Grüneisen model with no q-dependence of the anharmonic coupling constants offers a simple separation of the potential and the kinematics, and proved semiquantitative for Cu, Ag, and Au. A rule is reported for finding the most anharmonic phonon mode in fcc metals
Phonon anharmonicity of rutile TiO_2 studied by Raman spectrometry and molecular dynamics simulations
Raman spectra of rutile titanium dioxide (TiO_2) were measured at temperatures from 100 to 1150 K. Each Raman mode showed unique changes with temperature. Beyond the volume-dependent quasiharmonicity, the explicit anharmonicity was large. A new method was developed to fit the thermal broadenings and shifts of Raman peaks with a full calculation of the kinematics of three-phonon and four-phonon processes, allowing the cubic and quartic components of the anharmonicity to be identified for each Raman mode. A dominant role of phonon-phonon kinematics on phonon shifts and broadenings is reported. Force-field molecular dynamics calculations with the Fourier-transformed velocity autocorrelation method were also used to perform a quantitative study of anharmonic effects, successfully accounting for the anomalous phonon anharmonicity of the B_1_(g) mode
Balanced material selection approach of 316 stainless steel for high pressure hydrogen systems
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Anharmonicity-induced phonon broadening in aluminum at high temperatures
Thermal phonon broadening in aluminum was studied by theoretical and experimental methods. Using
second-order perturbation theory, phonon linewidths from the third-order anharmonicity were calculated from
first-principles density-functional theory (DFT) with the supercell finite-displacement method. The importance
of all three-phonon processes were assessed and individual phonon broadenings are presented. The good agreement between calculations and prior measurements of phonon linewidths at 300 K and new measurements of the phonon density of states to 750 K indicates that the third-order phonon-phonon interactions calculated from DFT can account for the lifetime broadenings of phonons in aluminum to at least 80% of its melting temperature
Compensation for Maritime Ecological Damages in China Judicial Practice
The Article discusses the judicial experience of compensation for maritime ecological damages in China. The discussion focusse on the verdict of “Tasman sea” oil spill case. Scope and methods of assessment of ecological damages are major part of the discussion. Because of the absence of legislation on compensation for maritime ecological damages, the verdict is a significant guide to similar case trial in the future
Agent-oriented Joint Decision Support for Data Owners in Auction-based Federated Learning
Auction-based Federated Learning (AFL) has attracted extensive research
interest due to its ability to motivate data owners (DOs) to join FL through
economic means. While many existing AFL methods focus on providing decision
support to model users (MUs) and the AFL auctioneer, decision support for data
owners remains open. To bridge this gap, we propose a first-of-its-kind
agent-oriented joint Pricing, Acceptance and Sub-delegation decision support
approach for data owners in AFL (PAS-AFL). By considering a DO's current
reputation, pending FL tasks, willingness to train FL models, and its trust
relationships with other DOs, it provides a systematic approach for a DO to
make joint decisions on AFL bid acceptance, task sub-delegation and pricing
based on Lyapunov optimization to maximize its utility. It is the first to
enable each DO to take on multiple FL tasks simultaneously to earn higher
income for DOs and enhance the throughput of FL tasks in the AFL ecosystem.
Extensive experiments based on six benchmarking datasets demonstrate
significant advantages of PAS-AFL compared to six alternative strategies,
beating the best baseline by 28.77% and 2.64% on average in terms of utility
and test accuracy of the resulting FL models, respectively
STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition
The spatial correlations and the temporal contexts are indispensable in
Electroencephalogram (EEG)-based emotion recognition. However, the learning of
complex spatial correlations among several channels is a challenging problem.
Besides, the temporal contexts learning is beneficial to emphasize the critical
EEG frames because the subjects only reach the prospective emotion during part
of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning
Network (STILN) to extract the discriminative features by capturing the spatial
correlations and temporal contexts. Specifically, the generated 2D power
topographic maps capture the dependencies among electrodes, and they are fed to
the CNN-based spatial feature extraction network. Furthermore, Convolutional
Block Attention Module (CBAM) recalibrates the weights of power topographic
maps to emphasize the crucial brain regions and frequency bands. Meanwhile,
Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately
combined to relieve the individual differences. In the temporal contexts
learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM)
network to capture the dependencies among the EEG frames. To validate the
effectiveness of the proposed method, subject-independent experiments are
conducted on the public DEAP dataset. The proposed method has achieved the
outstanding performance, and the accuracies of arousal and valence
classification have reached 0.6831 and 0.6752 respectively
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Federated learning (FL) enables collaborative machine learning across
distributed data owners, but data heterogeneity poses a challenge for model
calibration. While prior work focused on improving accuracy for non-iid data,
calibration remains under-explored. This study reveals existing FL aggregation
approaches lead to sub-optimal calibration, and theoretical analysis shows
despite constraining variance in clients' label distributions, global
calibration error is still asymptotically lower bounded. To address this, we
propose a novel Federated Calibration (FedCal) approach, emphasizing both local
and global calibration. It leverages client-specific scalers for local
calibration to effectively correct output misalignment without sacrificing
prediction accuracy. These scalers are then aggregated via weight averaging to
generate a global scaler, minimizing the global calibration error. Extensive
experiments demonstrate FedCal significantly outperforms the best-performing
baseline, reducing global calibration error by 47.66% on average.Comment: This paper has been accepted by ICML'2
Proline accumulation and metabolism-related genes expression profiles in Kosteletzkya virginica seedlings under salt stress
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